BACKGROUND
[0001] Conventional additive manufacturing processes have limited or no closed loop controls
and, therefore, rely on final material property assessments of a finished manufactured
part or product. Specifically, conventional additive manufacturing utilizes post deposition
analysis to provide these assessments.
BRIEF DESCRIPTION
[0002] In accordance with one or more embodiments, a material deposition process including
in situ sensor analysis of a component in a formation state is provided. The material
deposition process is implemented in part by an X-ray source and an X-ray detector
of an additive manufacturing machine producing the component. The material deposition
process includes sensing, by the X-ray source and the X-ray detector, in situ physical
properties of an area of interest of the component during a three-dimensional object
production. Compliance to specifications or defects are then detected in the in situ
physical properties with respect to pre-specified material requirements. The defects
are analyzed to determine corrective actions, and an updated three-dimensional object
production, which includes the corrective actions, is implemented to complete the
component.
[0003] In accordance with one or more embodiments or the material deposition process embodiment
above, the material deposition process can include implementing the three-dimensional
object production of the component according to a computer design file.
[0004] In accordance with one or more embodiments or any of the material deposition process
embodiments above, the material deposition process can include feeding forward and
back the corrective actions to the three-dimensional object production in real time
to generate the updated three-dimensional object production.
[0005] In accordance with one or more embodiments or any of the material deposition process
embodiments above, the at least one sensing device can include an X-ray source and
X-ray detector that together acquire a full or partial X-ray diffraction signal or
pattern that is analyzed to determine the in situ physical properties.
[0006] In accordance with one or more embodiments or any of the material deposition process
embodiments above, the in situ physical properties can potentially include: hardness,
local strain, yield strength, density, crystallite size, porosity, defect density,
crystalline orientation, texture, and compositional variation.
[0007] In accordance with one or more embodiments or any of the material deposition process
embodiments above, a compute device can include a processor executing software to
provide one or more process modeling, toolpath planning, defect detection, layer defect
detection, part defect detection, feedback control, scan path planning, decision making,
and process sensing operations for detecting the defects.
[0008] In accordance with one or more embodiments or any of the material deposition process
embodiments above, a compute device can include a database storing and providing the
pre-specified material requirements and a computer design file for detecting the defects
and implementing the three-dimensional object production.
[0009] In accordance with one or more embodiments, a system for implementing a three-dimensional
object production of a component via an additive manufacturing is provided. The system
includes an additive manufacturing machine including an X-ray source and an X-ray
detector. The system also includes a compute device including a processor and a memory.
The compute device is communicatively coupled to the additive manufacturing machine
and the X-ray source and the X-ray detector. The additive manufacturing machine and
the compute device provide in situ sensor analysis of the component while in a formation
state during a material deposition process of the additive manufacturing by sensing,
by the X-ray source and the X-ray detector, in situ physical properties of an area
of interest of the component during a three-dimensional object production. Compliance
to specifications or defects are then detected in the in situ physical properties
with respect to pre-specified material requirements. The defects are analyzed to determine
corrective actions, and an updated three-dimensional object production, which includes
the corrective actions, is implemented to complete the component.
[0010] In accordance with one or more embodiments or the system embodiment above, the three-dimensional
object production of the component can be implemented according to a computer design
file.
[0011] In accordance with one or more embodiments or any of the system embodiments above,
the compute device can feed forward and back the corrective actions to the three-dimensional
object production in real time to generate the updated three-dimensional object production.
[0012] In accordance with one or more embodiments or any of the system embodiments above,
the at least one sensing device can include an X-ray source and X-ray detector that
together acquire a full or partial X-ray diffraction signal or pattern that is analyzed
to determine the in situ physical properties.
[0013] In accordance with one or more embodiments or any of the system embodiments above,
the in situ physical properties can include hardness, local strain, yield strength,
density, crystallite size, porosity, defect density and compositional variation.
[0014] In accordance with one or more embodiments or any of the system embodiments above,
a compute device can include a processor executing software to provide one or more
process modeling, toolpath planning, defect detection, layer defect detection, part
defect detection, feedback control, scan path planning, decision making, and process
sensing operations for detecting the defects.
[0015] In accordance with one or more embodiments or any of the system embodiments above,
a compute device can include a database storing and providing the pre-specified material
requirements and a computer design file for detecting the defects and implementing
the three-dimensional object production.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The following descriptions should not be considered limiting in any way. With reference
to the accompanying drawings, like elements are numbered alike:
FIG. 1 depicts a system according to one or more embodiments;
FIG. 2 depicts a process flow according to one or more embodiments; and
FIG. 3 depicts a schematic flow according to one or more embodiments.
DETAILED DESCRIPTION
[0017] A detailed description of one or more embodiments of the disclosed apparatus and
method are presented herein by way of exemplification and not limitation with reference
to the Figures.
[0018] Turning now to an overview of technologies that are more specifically relevant to
aspects of the invention, as discussed above, conventional additive manufacturing
is a rapidly emerging means of flexible manufacturing. However, part-to-part variation,
non-uniformity of properties across finished manufactured parts or products, and local
or extended defects are significant concerns in utilizing conventional additive manufacturing
for high volume production. Most conventional additive manufacturing processes have
limited or no closed loop control. Therefore, post deposition analysis is employed
to assess only final material properties of the finished manufactured part or product
relative to pre-determined materials requirements. Further, post deposition analysis
does not allow a manufacturer to change or adapt properties during manufacturing.
[0019] Turning now to an overview of the aspects of the invention, one or more embodiments
of the invention address the above-described shortcomings of the conventional additive
manufacturing by providing, via a system, a method, and/or an apparatus (referred
to as a system, herein, for brevity), material deposition processes including in situ
sensor analysis. The in situ sensor analysis of the material deposition processes
extracts physical properties of a component in a formation state during its additive
manufacturing. The material deposition processes, then, feed forward and back these
physical properties to the additive manufacturing for continuous adaptability. The
technical effects and benefits of embodiments of the material deposition processes
herein include determining these physical properties during the formation state of
the component and, thus, enabling corrective actions, such as altering additive manufacturing
depositions, to achieve pre-specified material requirements.
[0020] Turning now to FIG. 1, a system 100 for implementing the teachings herein is shown
in according to one or more embodiments. The system 100 implements material deposition
processes including in situ sensor analysis.
[0021] In this embodiment, the system 100 includes a compute device 101. The compute device
101 can be an electronic, computer framework comprising and/or employing any number
and combination of computing device and networks utilizing various communication technologies,
as described herein. The compute device 101 can be easily scalable, extensible, and
modular, with the ability to change to different services or reconfigure some features
independently of others.
[0022] The compute device 101 has a processor 102, which can include one or more central
processing units (CPUs). The processor 102, also referred to as a processing circuit,
microprocessor, computing unit, is coupled via a system bus 103 to a system memory
104 and various other components. The system memory 104 includes read only memory
(ROM) and random access memory (RAM). The ROM is coupled to the system bus 103 and
may include a basic input/output system (BIOS), which controls certain basic functions
of the system 100. The RAM is read-write memory coupled to the system bus 103 for
use by the processor 102.
[0023] The compute device 101 includes a hard disk 107, which is an example of a tangible
storage medium readable executable by the processor 102. The hard disk 107 stores
software 108 and database 109. The software 108 is stored as instructions for execution
on the system 100 by the processor 102 (to perform process, such as the process flows
of FIGS. 2-3). The database 109 includes a set of values of qualitative or quantitative
variables organized in various data structures to support and be used by operations
of the software 108. Examples of operations provided by the software 108 include process
modeling, toolpath planning, defect detection, layer defect detection, part defect
detection, feedback control, scan path planning, decision making, and process sensing.
Examples of items stored on the database 109 include computer design files, pre-specified
material requirements, assessment models, assessment algorithms, and the like.
[0024] The compute device 101 includes one or more adapters (e.g., hard disk controllers,
network adapters, graphics adapters, etc.) that interconnect and support communications
between the processor 102, the system memory 104, the hard disk 107, and other components
of the translation system 100 (e.g., peripheral and external devices). In one or more
embodiments of the present invention, the one or more adapters can be connected to
one or more I/O buses that are connected to the system bus 103 via an intermediate
bus bridge, and the one or more I/O buses can utilize common protocols, such as the
Peripheral Component Interconnect (PCI).
[0025] The compute device 101 includes an interface adapter 110 interconnecting a keyboard,
a mouse, a speaker, a microphone, etc. to the system bus 103. The compute device 101
includes a display adapter 111 interconnecting the system bus 103 to a display. The
display adapter 111 (and/or the processor 102) can include a graphics controller to
provide graphics performance, such as a display and management of a graphic user interface.
A communications adapter 113 interconnects the system bus 103 with a network 120 enabling
the translation system 100 to communicate with other systems, devices, data, and software,
such as an additive manufacturing machine 130.
[0026] The system 100 includes the additive manufacturing machine 130, which further comprises
at least one sensor device 131, along with a processor, a memory, tool/feeder, and
other machining parts that are not shown for brevity. Note that while shown as separate
mechanisms communicating across the network 120, in accordance with one or more embodiment,
the compute device 101 and the additive manufacturing machine 130 can be integrated
into a single apparatus.
[0027] The additive manufacturing machine 130 is configured to manufacture a component 140
via the material deposition processes including in situ sensor analysis. In general,
additive manufacturing is a three-dimensional object production process utilizing
computer design file. In this regard, a variety of materials, ranging from polymer
composites, metals, ceramics, food, foams, gels, alloys, and the like, are deposited
by a tool or feeder according to the computer design file and heated by an electric
beam to set the material in place. The location of the deposited materials as the
tool or feeder moves according to the computer design file is referred to as a tool
path.
[0028] The at least one sensor device 131 can be any device including transducer and/or
a generator. In general, the transducer of the sensor device 131 can be any detector
converts variations in a physical quantity into an electrical signal. Examples of
physical quantities can include such as local strain, yield strength, density, crystallite
size, porosity, defect density, crystalline orientation, texture, compositional variation,
temperature, local porosity, optical density, reflectance (e.g., note that because
some of these quantities are difficult to extract, the sensor device 131 provides
added benefits for in situ analysis). The generator (also known as a source) of the
sensor device 131 can be any mechanism that, in response to electrical signals, generates
a wave, which itself is detectable or a reflection thereof is detectable by the transducer.
The at least one sensor device 131 can also communicate via any interface, such as
a controller area network (CAN), a local interconnect network (LIN), a direct I/O
interface, an analog to digital (A/D) interface, a digital to analog (D/A) interface,
or any other interface specific to the input, to the compute device 101 via the network
130, along with a processor, a memory, and machining parts of the additive manufacturing
machine 130. Note that the at least one sensor device 131 is representative of one
or more sensors of the same or varying type, each of which is capable of extracting
physical properties of the component 140 in a formation state during its additive
manufacturing. Example of the at least one sensor device 131 include, but are not
limited to, an X-ray, ultra-violet, visible light, near-infrared, short-wave infrared,
mid-wavelength infrared, long-wavelength infrared, and terahertz sensors, cameras,
and detectors. In accordance with one or more embodiments, the at least one sensor
device 131 includes an X-ray source and X-ray detector that together acquire a full
or partial X-ray diffraction signal or pattern that is analyzed to determine the in
situ physical properties. Further, the X-ray source and the X-ray detector can be
directed to detect a small portion of the full X-ray diffraction pattern, such that
a single peak with a particular intensity and width representing the detection.
[0029] Thus, as configured in FIG. 1, the operations of the software 108, the database 109,
and the additive manufacturing machine 130 (e.g., the system 100) are necessarily
rooted in the computational ability of the processors therein to overcome and address
the herein-described shortcomings of the conventional additive manufacturing. In this
regard, the software 108 and the data 109 improve manufacturing operations of the
additive manufacturing machine 130 by reducing and eliminating errors in manufacturing,
part-to-part variation, non-uniformity of properties, and local or extended defects
for high volume production.
[0030] FIG. 2 depicts a process flow 200 of according to one or more embodiments. The process
flow 200 is an example operation of implementing material deposition processes including
in situ sensor analysis of the component 140 in a formation state during its additive
manufacturing by the system 100.
[0031] The process flow 200 being at block 210, where the system 100 implements a material
deposition process to form the component 140 according to a computer design file.
In this regard, the additive manufacturing machine 130 can receive the computer design
file from the database 109 of the compute device 101 and begin three-dimensional object
production of the component 140.
[0032] At block 220, the system 100 senses in situ physical properties of the component
140 during the material deposition process. In accordance with one or more embodiments,
the at least one sensor device 131 is an X-ray detector that acquires an X-ray diffraction
(XRD) pattern while the component 140 is in a formation state (prior to completion).
Various parameters of the XRD pattern are analyzed by the software 108 of the compute
device 101 to determine the in situ physical properties or material parameters, such
as hardness, local strain, yield strength, density, crystallite size, porosity, defect
density and compositional variation (among other properties). The XRD pattern can
be taken from any area of interest of the component 140, as directed by the compute
device 101.
[0033] At block 230, the system 100 detects compliance to specifications or defects of the
in situ physical properties with respect to pre-specified material requirements. In
this regard, the compute device 101 can compare the pre-specified material requirements
of the database 109 to the in situ physical properties and determine if any defects
are present. At block 240, all defects are analyzed by the system 100 (e.g., by the
software 108 of the compute device 101) to determine whether corrective actions need
to be taken and what those corrective action should be.
[0034] At block 250, the system 100 feeds forward and back the corrective actions to the
material deposition process in real time for continuous adaptability, thereby updating
the material deposition process (e.g., altering additive manufacturing depositions)
to account for the defects and achieve pre-specified material requirements. At block
260, the system 100 implements the material deposition process with the corrective
actions to complete the manufacturing of the component 140.
[0035] Turning now to FIG. 3, a schematic flow 300 is depicted according to one or more
embodiments. The schematic flow 300 is an example operation of implementing in situ
monitoring of stress for a component (including in situ and post situ process controls)
by a system. The schematic flow 300 is executed by an additive manufacturing machine
301 comprising an X-ray source 302 and an X-ray detector 303 (e.g., an example of
the sensor device 131 of FIG. 1) and a computing device 304. To the extent that these
items overlap with the above system 100, further description is not provided for the
sake of brevity.
[0036] In general, the schematic flow 300 depicts a model 305 and a toolpath planning being
received by the additive manufacturing machine 301 and utilized in a production operation
315 to produce a component. Due to any number of factors during the production operation
315, the additive manufacturing machine 301 may produce a trending component 320.
The trending component 320 is note desired as a final component.
[0037] As shown in FIG. 3, the computing device 304 executes a sensing phase 330 through
a process sensing 322. The process sensing 322 includes receiving physical properties
of the component while the component is in a formation state. The X-ray source 302
generates X-rays so that an XRD pattern can be taken from any area of interest by
the X-ray detector 303. The physical properties are communicated by the X-ray detector
303 of the additive manufacturing machine 301, which is performing the in situ monitoring.
The process sensing 322 further include comparing pre-specified material requirements
to the in situ physical properties to provide comparison information. The sensing
phase 330 and the process sensing 322 can be implemented by software of the computing
device 304.
[0038] Next, the computing device 304 executes a detecting phase 340, which includes a process
defect detection 342, a layer defect detection 344, and a part defect detection 346.
The detecting phase 340 identifies defects with respect to errors in the process (e.g.,
the process defect detection 342), defect within one or more layers (e.g., the layer
defect detection 344), and defects across the component itself (e.g., the part defect
detection 346). The detecting phase 340 and operations therein can be implemented
by software of the computing device 304.
[0039] The computing device 304 also executes a reacting phase 350, which includes a feedback
control 352, a scan path planning 354, and a decision making 356. The reacting phase
350 and operations therein can be implemented by software of the computing device
304. The results of the reacting 350 phase include corrective actions that are provided
to the production operation 315. The corrected actions can include adjusting an area
of interest to determine where to perform the in situ monitoring (e.g., by the feedback
control 352), adjusting a scan path to accommodate or correct defects in the trending
component 320 (e.g., by the scan path planning 354), and determining material deposit
amounts to accommodate or correct defects in the trending component 320 (e.g., by
the decision making 356). The production operation 315 is improved by the corrective
actions from the computing device, such that the additive manufacturing machine 301
may now produce a desired component 350.
[0040] The term "about" is intended to include the degree of error associated with measurement
of the particular quantity based upon the equipment available at the time of filing
the application.
[0041] The terminology used herein is for the purpose of describing particular embodiments
only and is not intended to be limiting of the present disclosure. As used herein,
the singular forms "a", "an" and "the" are intended to include the plural forms as
well, unless the context clearly indicates otherwise. It will be further understood
that the terms "comprises" and/or "comprising," when used in this specification, specify
the presence of stated features, integers, steps, operations, elements, and/or components,
but do not preclude the presence or addition of one or more other features, integers,
steps, operations, element components, and/or groups thereof.
[0042] While the present disclosure has been described with reference to an exemplary embodiment
or embodiments, it will be understood by those skilled in the art that various changes
may be made and equivalents may be substituted for elements thereof without departing
from the scope of the invention as defined by the claims. In addition, many modifications
may be made to adapt a particular situation or material to the teachings of the present
disclosure without departing from the scope of the claims. Therefore, it is intended
that the present disclosure not be limited to the particular embodiment disclosed
as the best mode contemplated for carrying out this present disclosure, but that the
present disclosure will include all embodiments falling within the scope of the claims.
1. A material deposition process including in situ sensor analysis of a component in
a formation state, the material deposition process implemented in part by an X-ray
source (302) and an X-ray detector (303) of an additive manufacturing machine producing
the component, the material deposition process comprising:
sensing, by the X-ray source and the X-ray detector, in situ physical properties at
an area of interest of the component during a three-dimensional object production;
detecting compliance to specifications or defects in the in situ physical properties
with respect to pre-specified material requirements;
analyzing the defects to determine corrective actions; and
implementing an updated three-dimensional object production, which includes the corrective
actions, to complete the component.
2. The material deposition process of claim 1, the material deposition process comprising:
implementing the three-dimensional object production of the component according to
a computer design file.
3. The material deposition process of claim 1 or 2, the material deposition process comprising:
feeding forward and back the corrective actions to the three-dimensional object production
in real time to generate the updated three-dimensional object production.
4. The material deposition process of any preceding claim, wherein the X-ray source and
the X-ray detector together acquire a full or partial X-ray diffraction signal or
pattern that is analyzed to determine the in situ physical properties.
5. The material deposition process of any preceding claim, wherein the in situ physical
properties comprise hardness, local strain, yield strength, density, crystallite size,
porosity, defect density, crystalline orientation, texture, and compositional variation.
6. The material deposition process of any preceding claim, wherein a compute device (101)
comprises a processor (102) executing software to provide one or more process modeling,
toolpath planning, defect detection, layer defect detection, part defect detection,
feedback control, scan path planning, decision making, and process sensing operations
for detecting the defects.
7. The material deposition process of any preceding claim, wherein a compute device comprises
a database (109) storing and providing the pre-specified material requirements and
a computer design file for detecting the defects and implementing the three-dimensional
object production.
8. A system for implementing a three-dimensional object production of a component via
an additive manufacturing, the system comprising:
an additive manufacturing machine (130) comprising an X-ray source (302) and an X-ray
detector (303); and
a compute device (101) comprising a processor (102) and a memory (104), the compute
device being communicatively coupled to the additive manufacturing machine and the
X-ray source and the X-ray detector,
wherein the additive manufacturing machine and the compute device provide in situ
sensor analysis of the component while in a formation state during a material deposition
process of the additive manufacturing by:
sensing, by the X-ray source and the X-ray detector, in situ physical properties at
an area of interest of the component during the three-dimensional object production;
detecting compliance to specifications or defects in the in situ physical properties
with respect to pre-specified material requirements;
analyzing the defects to determine corrective actions;
implementing an updated three-dimensional object production, which includes the corrective
actions, to complete the component.
9. The system of claim 8, wherein the three-dimensional object production of the component
is implemented according to a computer design file.
10. The system of claim 8 or 9, wherein the compute device feeds forward and back the
corrective actions to the three-dimensional object production in real time to generate
the updated three-dimensional object production.
11. The system of claim 8, 9 or 10, wherein the X-ray source and the X-ray detector together
acquire a full or partial X-ray diffraction signal or pattern that is analyzed to
determine the in situ physical properties.
12. The system of claim 11, wherein the in situ physical properties comprise hardness,
local strain, yield strength, density, crystallite size, porosity, defect density
and compositional variation.
13. The system of any of claims 8 to 12, wherein a compute device comprises a processor
(102) executing software to provide one or more process modeling, toolpath planning,
defect detection, layer defect detection, part defect detection, feedback control,
scan path planning, decision making, and process sensing operations for detecting
the defects.
14. The system of any of claims 8 to 13, wherein a compute device comprises a database
(109) storing and providing the pre-specified material requirements and a computer
design file for detecting the defects and implementing the three-dimensional object
production.